
What you would learn in Image Recognition for Beginners using CNN in R Studio course?
Are you looking for a comprehensive Convolutional Neural Network (CNN) course that will teach the basics needed to construct the Image Recognition model in R, isn't it?
You've found the perfect Convolutional Neural Networks class!
When you've completed this program, you can:
Determine what the Image Recognition problems which can be solved with CNN Models are.
Create CNN models using R with the help of Keras and Tensorflow libraries, and then analyze their outcomes.
Be confident in your practice, and discuss and comprehend Deep Learning concepts.
Know the basics of the concepts of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16, and many more.
What will this course do to assist you?
A verified Certificate of Completion is given to every student who completes the Convolutional Neural Networks course.
Suppose you're an analyst or ML scientist or student looking to understand how to apply Deep learning to real-world problems with image recognition. In that case, This course will provide you a solid foundation by teaching the latest ideas associated with Deep Learning and their application in R without becoming too Mathematical.
The reason why you should take this program?
This course will cover all the steps you must take to develop an imaging recognition algorithm using Convolutional Neural Networks.
The majority of courses focus on teaching the way to conduct the analysis. Still, we believe that having a solid conceptual understanding of the concepts can help us create an effective model. When the analysis is completed, it is expected to determine how well the model is and then interpret the results so that they are helpful in the business.
Course Content:
- Gain a thorough understanding of Convolutional Neural Networks (CNN) and Deep Learning
- Develop an end-to-end Image recognition project using R
- Learn how to use Keras Libraries and the Tensorflow Library
- Make use of Artificial Neural Networks (ANN) to predict future events
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